About

Description

Carnegie Mellon University’s Delphi Research Group derives it’s “Combined” indicator from multiple data sources that include:

The higher the combined indicator value, the greater the signaling of higher COVID-19 prevalence in that metropolitan area (MSA). The value has no upper bound, but for reference, here are some of the combined indicator values from some of the hotspots around the country as of July 3rd, 2020.

Only metropolitan areas in Indiana with sufficient data have combined indicator values calculated, so not all areas are shown in the map. More details about the methodology behind this project can be found on their website.

Charts

Line

The grouped line chart shows historic combined indicator values for each MSA.

Dumbbell

The dumbbell chart shows the combined indicator value (gray) and the 95% confidence interval.

Map

The map shows the metropolitan statistical areas that have calculated combined indicator values.

Dashboard

Row

Historic Values

Row

Metropolitan Statistical Areas

Uncertainty

Row

Cases per 100K population and Positivity Rates

---
title: "Carnegie Mellon's Combined Indicator for Indiana"
output:
   flexdashboard::flex_dashboard:
      orientation: rows
      vertical_layout: scroll
      social: menu
      source_code: embed
      theme: yeti
      # css: covidcast-style.css
      # favicon: images/ind-state-fav.png
      # logo: images/rsz_ind-state-logo.png
      navbar:
         - {icon: "fa-arrow-alt-circle-left", href: "https://ercbk.github.io/Indiana-COVID-19-Website/static.html", align: right, title: "Back to Tracker"}
---



```{r setup-data}
pacman::p_load(extrafont, dplyr, glue, leaflet, leaflet.extras, plotly, crosstalk, htmltools, reactable, dataui)

# clean combined indicator datasets for line, leaflet, and dumbbell charts
ci_clean_line <- readr::read_rds(glue("{rprojroot::find_rstudio_root_file()}/data/dash-ci-line.rds"))
ci_clean_leaf <- readr::read_rds(glue("{rprojroot::find_rstudio_root_file()}/data/dash-ci-leaf.rds"))
ci_clean_db <- readr::read_rds(glue("{rprojroot::find_rstudio_root_file()}/data/dash-ci-db.rds"))

# reactable/sparkline data
react_dat <- readr::read_rds(glue("{rprojroot::find_rstudio_root_file()}/data/dash-case-pos.rds"))
# positive rate sparkline palette vector
posrate_col <- readr::read_rds(glue("{rprojroot::find_rstudio_root_file()}/data/dash-posrate_pal.rds"))


```




```{r shared}

# {crosstalk} functions so charts can react to selections
ci_shared_line <- SharedData$new(ci_clean_line,
                                 ~name,
                                 group = "covidcast")
ci_shared_leaf <- SharedData$new(ci_clean_leaf,
                                 ~name,
                                 group = "covidcast")
ci_shared_db <- SharedData$new(ci_clean_db,
                                 ~name,
                                 group = "covidcast")

```




About {data-icon="fa-question-circle"}
=====================================


Description

Carnegie Mellon University's Delphi Research Group derives it's "Combined" indicator from multiple data sources that include:
  • Doctor Visits - Percentage of doctor's visits that are due to COVID-like symptoms
  • Hospital Admissions - Daily hospital admissions with COVID diagnostic codes.
  • Symptoms (Facebook) - Random Facebook users are directed to a survey that asks for the number of household members with COVID-like symptoms.
  • Symptoms in Community (Facebook) - Same as for "Symptoms" but asks about people they know outside of their household.
  • Hours Away from Home (SafeGraph) - Uses mobile phone location data to estimate the proportion of people outside their homes during daytime hours.
  • Search Trends (Google) - Google searchs for COVID-related topics relative to an area's population

The higher the combined indicator value, the greater the signaling of higher COVID-19 prevalence in that [metropolitan area (MSA)](https://www.census.gov/programs-surveys/metro-micro/about.html). The value has no upper bound, but for reference, here are some of the combined indicator values from some of the hotspots around the country as of July 3rd, 2020.

  • San Antonio, Texas: 2.69
  • Phoenix, Arizona: 1.92
  • Naples, Florida: 2.76
Only metropolitan areas in Indiana with sufficient data have combined indicator values calculated, so not all areas are shown in the map. More details about the methodology behind this project can be found on their [website](https://covidcast.cmu.edu/index.html?sensor=doctor-visits-smoothed_adj_cli&level=county&region=42003&date=20200701&signalType=value).

Charts

Line

The grouped line chart shows historic combined indicator values for each MSA.
  • Clicking a line highlights the line and deemphasizes the others. Multiple lines can be highlighted. Highlighting a line also has the same effect on that MSA's error bar in the dumbbell chart.
  • Clicking on the brush color in the top-left allows you to change the default color (red) to either blue, green, or purple.
  • Drawing a window around a section of the chart zooms into that time window
  • Pan or Zoom-out (toolbar) can be useful for looking at edge values
  • Home button (toolbar) or double-clicking in the plot area resets the chart
  • Compare-data-on-hover button (toolbar) shows every label for each MSA as you move your cursor across the days.

Dumbbell

The dumbbell chart shows the combined indicator value (gray) and the 95% confidence interval.
  • Clicking on a point, highlights that MSA's value and deemphisizes the others. It also has the same effect for the MSA's values in the line chart. Multiple MSA values may be selected.
  • Double-clicking in the plot area resets the chart.

Map

The map shows the metropolitan statistical areas that have calculated combined indicator values.
  • Hovering over an area highlights the borders of that area.
  • Clicking on an area shows its estimated combined indicator value.
  • Reset button (upper-left) resets the map view. It's a bit buggy, so you might need to refresh the webpage instead.
Dashboard {data-icon="glyphicon-stats"} ===================================== Row {data-height=360} ------------------------------------- ### Historic Values ```{r} # styling for axes labels and ticks x_style_line <- list( title = "Combined indicator over time", titlefont = list( color = "black", face = "bold", size = 18 ), tickfont = list( face = "bold", size = 14 ) ) y_style_line <- list( title = "", tickfont = list( color = "black", face = "bold", size = 16 ) ) # tried to get hoverlabel to have background color conditional on value (bgcolor) but failed. Text color would depend on darkness of bgcolor. # group_by was screwing up something, but I forget (maybe crosstalk). Split does same/similar thing ci_shared_line %>% # group_by(name) %>% # "~" are for variables in df plot_ly(x = ~time_value, y = ~value, color = I("black"), split = ~name, text = ~name, # part removes "trace" from text hovertemplate = paste( "%{x}
", "%{text}
", "Combined: %{y}", "" ), hoverlabel = list( #bgcolor = ~color, align = 'left', bordercolor = 'transparent', font = list( # need b/c of bug with setting border to 'transparent' #color = ~text_col color = 'white' ) ) ) %>% add_lines(showlegend = FALSE) %>% layout(xaxis = x_style_line, yaxis = y_style_line, showlegend = FALSE, # sets global value for font family font = list(family = "Roboto"), # x-axis tick labels get cut off with default margin margin = list(b = 90) ) %>% highlight(dynamic = TRUE, persistent = TRUE) ``` Row {data-height=625} ------------------------------------- ### Metropolitan Statistical Areas ```{r} # needed because leaflet legend default direction of values is backwards pal_rev <- leaflet::colorNumeric("YlOrRd", domain = seq(2.50, 0.00, by = -0.10), reverse = TRUE) # minzoom is the maximum you can zoomout; viceversa for maxzoom; 0 would be for zooming all the out leaflet(data = ci_shared_leaf, options = leafletOptions(minZoom = 6.5, maxZoom = 18, # remove caption attributionControl = FALSE)) %>% # black and white basemap addProviderTiles("Stamen.Toner") %>% # sets starting point; coords for center of Indiana setView(lat = 40.2672, lng = 86.1349, zoom = 7) %>% # set panning range; if user tries to go beyond, it springs back setMaxBounds(lat1 = 37.62598, lng1 = -89.53418, lat2 = 42.64689, lng2 = -83.05625) %>% # add msa shapes addPolygons(# weight is thickness of stroke weight = 2, smoothFactor = 0.5, opacity = 1.0, fillOpacity = 0.5, color = ~color, popup = ~popup, # popupOptions = , # bringtofront makes highlight stroke standout more highlightOptions = highlightOptions(color = "black", weight = 2, bringToFront = TRUE)) %>% addLegend("bottomleft", pal = pal_rev, opacity = 1, values = c(2.5, 0.5), # reverses direction of values in legend labFormat = labelFormat(transform = function(x) sort(x, decreasing = TRUE)) ) %>% # {leaflet.extras} addResetMapButton() ``` ### Uncertainty ```{r} # styling for axes labels and ticks x_style_db <- list( title = "Combined indicator with uncertainty range", titlefont = list( color = "black", face = "bold", size = 18 ), tickfont = list( face = "bold", size = 14 ) ) y_style_db <- list( title = "", tickfont = list( color = "black", face = "bold", size = 16 ) ) ci_shared_db %>% plot_ly(y = ~name, text = ~name, # part removes "trace" from text hovertemplate = paste( "%{text}
", "Combined: %{x}", "" ), hoverlabel = list( bordercolor = 'transparent', font = list( # need b/c of bug with setting border to 'transparent' color = ~text_col ) )) %>% # removes toolbar config(displayModeBar = F) %>% add_segments( # "~" are for variables in df x = ~lower, xend = ~upper, yend = ~name, # "I" means asis color = I("gray") ) %>% add_markers( x = ~lower, color = ~I(lower_col), size = I(65) ) %>% add_markers( x = ~upper, color = ~I(upper_col), size = I(65) ) %>% add_markers( x = ~value, color = I("gray"), size = I(40) ) %>% layout(xaxis = x_style_db, yaxis = y_style_db, showlegend = FALSE, # sets global value for font family font = list(family = "Roboto") ) %>% highlight(persistent = TRUE) ``` Row ------------------------------------- ### Cases per 100K population and Positivity Rates ```{r reactable} # sparkline column for cases per 100k trend cases_spark <- function(class = NULL, ...) { colDef( name = "Cases per 100K Trend", cell = dui_for_reactable( dui_sparkline( data = htmlwidgets::JS("cellInfo.value.cases_list"), valueAccessor = htmlwidgets::JS("(d) => d.cases[0]"), renderTooltip = htmlwidgets::JS( htmltools::HTML( "function (_ref) { var datum = _ref.datum; return React.createElement( 'div', {style: {margin: 0, padding: 0}}, datum.date && React.createElement( 'div', {style: { backgroundColor: 'black', color: 'white', padding: '4px 0px', margin: 0, textAlign: 'center' }}, // good reference https://observablehq.com/@mbostock/date-formatting // for built-in JavaScript formatting // but need to convert string to Date with new Date() new Date(datum.date).toLocaleString(undefined, { 'month': 'numeric', 'day': '2-digit' }) ), React.createElement( 'div', {style: {fontWeight: 'bold', fontSize: '1.2em', padding: '6px 0'}}, datum.y ? datum.y.toLocaleString(undefined, {maximumFractionDigits: 0}) : '--' ) ); }" )), components = list( dui_sparklineseries( showLine = FALSE, showArea = TRUE, fill = htmlwidgets::JS("cellInfo.original.cases_color"), fillOpacity = 0.6 ), dui_sparkpointseries( points = list("max"), fill = htmlwidgets::JS("cellInfo.original.cases_color"), stroke = htmlwidgets::JS("cellInfo.original.cases_color"), renderLabel = htmlwidgets::JS("(d) => d.toFixed(0)"), labelPosition = "left", size = 3 ) ) ) ) ) } posrate_spark <- function(class = NULL, ...){ colDef( name = "Positive Test Rate Trend", cell = function(value, index) { if(is.null(value)) return(dui_sparkline()) dui_sparkline( data = value[[1]], valueAccessor = htmlwidgets::JS("(d) => d.posRate"), renderTooltip = htmlwidgets::JS( htmltools::HTML( "function (_ref) { var datum = _ref.datum; return React.createElement( 'div', {style: {margin: 0, padding: 0}}, datum.endDate && React.createElement( 'div', {style: { backgroundColor: 'black', color: 'white', padding: '4px 0px', margin: 0, textAlign: 'center' }}, new Date(datum.endDate).toLocaleString(undefined, { 'month': 'numeric', 'day': '2-digit' }) ), React.createElement( 'div', {style: {fontWeight: 'bold', fontSize: '1.2em', padding: '6px 0'}}, datum.y ? datum.y.toLocaleString(undefined, {maximumFractionDigits: 0, style: 'percent'}) : '--' ) ); }" ) ), components = list( # dui_sparkpatternlines( # id = "band_pattern_misc", # height = 4, # width = 4, # stroke = posrate_col[index], # strokeWidth = 1, # orientation = list('diagonal') # ), # dui_sparkbandline( # band = list( from = list( y = 0 ), to = list( y = 0.05 ) ), # fill = "url(#band_pattern_misc)" # ), dui_sparklineseries( stroke = posrate_col[index] ) ) ) } ) } reactable( data = react_dat, borderless = TRUE, style = list(fontSize = "18px"), defaultPageSize = 5, defaultSortOrder = "desc", defaultSorted = "cases_100k", defaultColDef = colDef( align = "center", headerStyle = "align-self: flex-end; font-weight:normal;" ), rowStyle = list( alignItems = "center", # add back border here borderBottom = "1px solid lightgray" ), highlight = TRUE, columns = list( cases_color = colDef(show = FALSE), # pos_color = colDef(show = FALSE), msa = colDef( name = "Metropolitan Statistical Area" ), cases_100k = colDef( name = "Cases per 100K", ), pos_rate = colDef( name = "Positive Test Rate", na = "–", format = colFormat(percent = TRUE, digits = 1) ), # casesList = cases_spark(), posList = posrate_spark(), casesList = colDef(show = FALSE)#, # posList = colDef(show = FALSE) ) ) #%>% #dui_add_reactable_dep() ```